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MEMS Gas Sensor Array Fault Diagnostic Unit for Microsystem Applications
编号：153 访问权限：仅限参会人 更新：2024-10-23 10:02:35 浏览：168次 张贴报告

报告开始：暂无开始时间（Asia/Shanghai）

报告时间：暂无持续时间

所在会场：[暂无会议] [暂无会议段]

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摘要
With the rapid development of the Internet of Things (IoT) and environment sensing systems, environment sensing intelligent microsystems based on microelectrome- chanical systems (MEMS) sensor arrays have vast application prospects and advantages in the fields of environmental monitoring, medical diagnosis, and battlefield sensing. The machine olfaction-oriented environment sensing intelligent microsystem is a microsystem that combines MEMS gas sensor array and pattern recognition algorithms to monitor the gases in the environment in real time, and its working performance is seriously dependent on the accuracy of the measurement signals from the MEMS gas sensor array. The performance of the microsystem relies heavily on the accuracy of the measurement signals from the MEMS gas sensor array. Due to the characteristics of the sensitive materials and the integrated fabrication process, factors such as environmental changes may cause failures of the MEMS gas sensor array, resulting in abnormal operation of the entire microsystem. Therefore, this paper designs a MEMS gas sensor array fault diagnostic unit for microsystem applications with the size of 19mm*19mm*3mm, which can identify five kinds of sensor faults, namely, shock fault, bias fault, constant output fault, power-down fault, and accuracy degradation fault, and has fault isolation and fault localisation functions, which can provide the basis for the recovery of faulty data of MEMS gas sensors. The multi-task fault diagnosis algorithm based on 1D CNN -LSTM on the fault diagnosis unit has a fault recognition accuracy of 99.93%, a fault isolation accuracy of 99.43%, and a fault localisation accuracy of 98.12%, occupies 194k of FLASH and 20k of SRAM, and has an average running time of 234ms, and runs with a power consumption of 11.58mW.
 
关键词
MEMS gas sensor array,microsystems,fault diagnosis,multitask learning,embedded deployment
报告人
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稿件作者
FuJie Harbin Institute of Technology；Department of Measurement and Control Engineering at the School of Electronics and Information Engineering
YangJian No. 703 Research Institute of CSSC
FuHongshuo Harbin Institute of Technology；Department of Measurement and Control Engineering at the School of Electronics and Information Engineering
LiuBing Harbin Institute of Technology；Department of Measurement and Control Engineering at the School of Electronics and Information Engineering
ZhengWenbin Harbin Institute of Technology；School of Electronics and Information Engineering； Harbin 150080； P.R. China
FuPing Harbin Institute of Technology；Department of Measurement and Control Engineering at the School of Electronics and Information Engineering
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重要日期

  • 会议日期

    10月31日

    2024

    至

    11月03日

    2024

  • 09月30日 2024

    初稿截稿日期

  • 11月12日 2024

    注册截止日期

主办单位
Anhui University
Xi’an Jiaotong University
Harbin Institute of Technology
IEEE Instrumentation & Measurement Society
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